
# Import packages
import pandas as pd
import numpy as np
import os
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import cufflinks as cf
cf.go_offline()

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.python.ops.gen_array_ops import size

from keras.layers.advanced_activations import LeakyReLU

from sklearn.metrics import mean_absolute_error as mae
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score


BATH_PATH = os.path.dirname(os.path.abspath(__file__))
data_path = os.path.join(BATH_PATH, "..", "data")


# Read data
def get_PET_history(floor):
    path = os.path.join(data_path, F"PET_{floor}F_data-20210401_20210831.csv")
    PET_data = pd.read_csv(path)
    PET_data["Time"] = pd.to_datetime(PET_data["Time"])
    PET_data.sort_values(by="Time", inplace=True)
    PET_data.set_index("Time", inplace=True)
    PET_data = PET_data.loc[~PET_data.index.duplicated(keep="first")]
    return PET_data


def get_FCU_history(floor):
    path = os.path.join(data_path, F"FCU_{floor}F_data-20210201_20210831.csv")
    FCU_data = pd.read_csv(path)
    FCU_data["Time"] = pd.to_datetime(FCU_data["Time"])
    FCU_data.sort_values(by="Time", inplace=True)
    FCU_data.set_index("Time", inplace=True)
    FCU_data = FCU_data.loc[~FCU_data.index.duplicated(keep='first')]
    return FCU_data


def get_AHU_history(floor):
    path = os.path.join(data_path, F"AHU_{floor}F_data-20210201_20210831.csv")
    AHU_data = pd.read_csv(path)
    AHU_data["Time"] = pd.to_datetime(AHU_data["Time"])
    AHU_data.sort_values(by="Time", inplace=True)
    AHU_data.set_index("Time", inplace=True)
    AHU_data = AHU_data.loc[~AHU_data.index.duplicated(keep='first')]
    return AHU_data


def get_CO2_history(floor):
    path = os.path.join(data_path, F"CDS_{floor}F_data-20210201_20210831.csv")
    CDS_data = pd.read_csv(path)
    CDS_data["Time"] = pd.to_datetime(CDS_data["Time"])
    CDS_data.sort_values(by="Time", inplace=True)
    CDS_data.set_index("Time", inplace=True)
    if floor == 8:
        CDS_data = CDS_data.drop("CDS_108_8-CO2", axis=1) # deviation in this sensor data 
    CDS_data = CDS_data.loc[~CDS_data.index.duplicated(keep='first')]
    CDS_data["Floor_CO2_mean"] = CDS_data.mean(axis=1)
    return CDS_data.loc[:, ["Floor_CO2_mean"]]


def get_CWB_history():
    path = os.path.join(data_path, "CWB_data-20210201_20210831.csv")
    CWB_data = pd.read_csv(path)
    CWB_data["Time"] = pd.to_datetime(CWB_data["Time"])
    CWB_data.sort_values(by="Time", inplace=True)
    CWB_data.set_index("Time", inplace=True)
    CWB_data = CWB_data.loc[~CWB_data.index.duplicated(keep='first')]
    return CWB_data


def merge_data(floor):
    CWB_data = get_CWB_history()
    FCU_data = get_FCU_history(floor)
    AHU_data = get_AHU_history(floor)
    CDS_data = get_CO2_history(floor)
    PET_data = get_PET_history(floor)
    ALL_data = pd.concat([CWB_data, FCU_data, AHU_data, CDS_data, PET_data], axis=1, join="outer")
    # Drop insignificant or noisy variable 
    ALL_data = ALL_data.drop(["RH", "GloblRad", "PAH-OA_FLOW", "PAH-FAN_RUN_CMD", "PAH-DMP_POS", "PET_Target"], axis=1)
    return ALL_data.dropna()

floor = 8
display(merge_data(floor))
merge_data(floor).iplot()


# Create timeseries features (here is just the sample not the real structure in the field)
def get_train_feature(target, floor, future_step=1):
    # Preparing features (can also add some features if you'd like to try other models)
    data = merge_data(floor)
    
    # data.insert(loc=0, column="DoY", value=(data.index.dayofyear).values) # seems not significant
    data.insert(loc=1, column="Hour", value=(data.index.hour).values + (pd.Series(data.index).dt.minute).values/60)
    data.insert(loc=2, column="DoW", value=(data.index.dayofweek).values)
    data.insert(loc=3, column="HoW", value=(data["Hour"] + 24 * data.index.dayofweek).values)

    # features set (add lag features for future prediction: the [t-1] situation leads to [t] outcome)
    for col in data.loc[:, ~data.columns.str.contains(target)].columns:
        data.loc[:, F"{col}-lag"] = data.loc[:, col].shift(future_step)
    
    # target can also shift as lag features (but seems not significant)
    # data.loc[:, F"{target}-lag"] = data.loc[:, target].shift(future_step)

    return data.dropna()

# Scale data may get some help
def data_scale(dataset, scale_type="MinMax"):
    if scale_type =="MinMax":
        data_min = dataset.min()
        data_max = dataset.max()
        scaled_df = (dataset - data_min) / (data_max - data_min)
        pars = {"data_min": data_min, "data_max": data_max}
        return scaled_df, pars
    if scale_type =="normalize":
        data_mean = dataset.mean()
        data_std = dataset.std()
        normalized_df = (dataset - data_mean) / data_std
        pars = {"data_mean": data_mean, "data_std": data_std}
        return normalized_df, pars

# Split train, valid and test data
def generator(data, start, end):
    X = data.loc[start:end, ~data.columns.str.contains(target)]
    Y = data.loc[start:end, data.columns.str.contains(target)]
    return X, Y

floor = 8
target = "SP_Target"
future_step = 6 * 1 # timestep is 10 min, 6 timestep for 1 hr lag-feature

# data = get_train_feature(target, floor, future_step) # can also try training data without scaled
data = data_scale(get_train_feature(target, floor, future_step))[0]
pars = data_scale(get_train_feature(target, floor, future_step))[1]

# train data
start = "2021-04-01"
end = "2021-06-30"
_train = generator(data, start, end)
train_x = _train[0]
train_y = _train[1]

# valid data
start = "2021-05-31"
end = "2021-07-31"
_val = generator(data, start, end)
val_x = _val[0]
val_y = _val[1]

# test data
start = "2021-06-30"
end = "2021-08-31"
_test = generator(data, start, end)
test_x = _test[0]
test_y = _test[1]

# all data
start = "2021-04-01"
end = "2021-08-31"
_all = generator(data, start, end)
all_x = _all[0]
all_y = _all[1]

display(all_x)
display(all_y)


# Model train (the model here is one of the optimized sample)
model_summary = pd.DataFrame()
Epochs = 30
Batch = 6 * 24 * 7

tf.random.set_seed(221)
model = Sequential()
model.add(Dense(units=8, input_dim=train_x.shape[1], activation="relu"))
model.add(Dense(units=16, activation="selu"))
model.add(Dense(units=32, activation=LeakyReLU()))
model.add(Dense(units=16, activation="selu"))
model.add(Dense(units=8, activation="relu"))
model.add(Dense(units=1))

opt = keras.optimizers.Adam()
model.compile(loss="mae", optimizer=opt, metrics=["mse"])
model.fit(train_x, train_y, epochs=Epochs, batch_size=Batch, validation_data=(val_x, val_y))

y_pred = model.predict(test_x)
test_y["pred"] = y_pred[:, 0]
test_y[target] = test_y[target]
test_y["pred"] = test_y["pred"]

fig = go.Figure()
fig.add_trace(go.Scatter(name="real", x=test_y.index, y=test_y[target]))
fig.add_trace(go.Scatter(name="pred", x=test_y.index, y=test_y["pred"]))
fig.show()

test_y = test_y.dropna()

print("r2: ", round(r2_score(test_y[target], test_y["pred"]), 2))
print("MAE: ", round(mae(test_y[target], test_y["pred"]), 2))
print("RMSE: ", round(mse(test_y[target], test_y["pred"]), 2))

model.summary()

Epoch 1/30
13/13 [==============================] - 1s 14ms/step - loss: 0.1130 - mse: 0.0227 - val_loss: 0.0777 - val_mse: 0.0092
Epoch 2/30
13/13 [==============================] - 0s 4ms/step - loss: 0.0899 - mse: 0.0159 - val_loss: 0.0545 - val_mse: 0.0066
Epoch 3/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0819 - mse: 0.0144 - val_loss: 0.0505 - val_mse: 0.0059
Epoch 4/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0765 - mse: 0.0133 - val_loss: 0.0484 - val_mse: 0.0056
Epoch 5/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0722 - mse: 0.0122 - val_loss: 0.0471 - val_mse: 0.0052
Epoch 6/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0685 - mse: 0.0113 - val_loss: 0.0462 - val_mse: 0.0048
Epoch 7/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0656 - mse: 0.0106 - val_loss: 0.0441 - val_mse: 0.0044
Epoch 8/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0633 - mse: 0.0101 - val_loss: 0.0424 - val_mse: 0.0041
Epoch 9/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0618 - mse: 0.0099 - val_loss: 0.0411 - val_mse: 0.0038
Epoch 10/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0607 - mse: 0.0095 - val_loss: 0.0401 - val_mse: 0.0037
Epoch 11/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0598 - mse: 0.0094 - val_loss: 0.0399 - val_mse: 0.0036
Epoch 12/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0589 - mse: 0.0094 - val_loss: 0.0397 - val_mse: 0.0036
Epoch 13/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0580 - mse: 0.0092 - val_loss: 0.0375 - val_mse: 0.0033
Epoch 14/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0569 - mse: 0.0091 - val_loss: 0.0372 - val_mse: 0.0032
Epoch 15/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0564 - mse: 0.0090 - val_loss: 0.0369 - val_mse: 0.0030
Epoch 16/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0562 - mse: 0.0089 - val_loss: 0.0361 - val_mse: 0.0031
Epoch 17/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0554 - mse: 0.0088 - val_loss: 0.0348 - val_mse: 0.0029
Epoch 18/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0553 - mse: 0.0088 - val_loss: 0.0342 - val_mse: 0.0028
Epoch 19/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0546 - mse: 0.0087 - val_loss: 0.0350 - val_mse: 0.0029
Epoch 20/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0545 - mse: 0.0087 - val_loss: 0.0329 - val_mse: 0.0027
Epoch 21/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0537 - mse: 0.0086 - val_loss: 0.0337 - val_mse: 0.0027
Epoch 22/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0533 - mse: 0.0086 - val_loss: 0.0327 - val_mse: 0.0027
Epoch 23/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0529 - mse: 0.0085 - val_loss: 0.0339 - val_mse: 0.0027
Epoch 24/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0531 - mse: 0.0085 - val_loss: 0.0318 - val_mse: 0.0026
Epoch 25/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0525 - mse: 0.0084 - val_loss: 0.0314 - val_mse: 0.0026
Epoch 26/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0527 - mse: 0.0084 - val_loss: 0.0311 - val_mse: 0.0026
Epoch 27/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0518 - mse: 0.0083 - val_loss: 0.0303 - val_mse: 0.0025
Epoch 28/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0530 - mse: 0.0083 - val_loss: 0.0341 - val_mse: 0.0026
Epoch 29/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0532 - mse: 0.0082 - val_loss: 0.0308 - val_mse: 0.0025
Epoch 30/30
13/13 [==============================] - 0s 3ms/step - loss: 0.0509 - mse: 0.0081 - val_loss: 0.0298 - val_mse: 0.0024

r2:  0.64
MAE:  0.04
RMSE:  0.0
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 8)                 216       
_________________________________________________________________
dense_1 (Dense)              (None, 16)                144       
_________________________________________________________________
dense_2 (Dense)              (None, 32)                544       
_________________________________________________________________
dense_3 (Dense)              (None, 16)                528       
_________________________________________________________________
dense_4 (Dense)              (None, 8)                 136       
_________________________________________________________________
dense_5 (Dense)              (None, 1)                 9         
=================================================================
Total params: 1,577
Trainable params: 1,577
Non-trainable params: 0
_________________________________________________________________


plot_df = test_y * (pars["data_max"][target] - pars["data_min"][target]) + pars["data_min"][target]
plot_df.iplot(title=F"{target}-real vs prediction")


# this model only have to be precise at the time we need
# for such a special application, here can use schedule rules to optimize predictive strategy
plot_df["pred"] = np.where(np.logical_or(plot_df.index.dayofweek>4, np.logical_or(plot_df.index.hour<7, plot_df.index.hour>21)), 25, plot_df["pred"])
plot_df.iplot(title=F"{target}-real vs prediction")

# the control strategy of HVAC Setpoint temperature assure the indoor thermal comfort
print("r2: ", round(r2_score(plot_df[target], plot_df["pred"]), 2))
print("MAE: ", round(mae(plot_df[target], plot_df["pred"]), 2))
print("RMSE: ", round(mse(plot_df[target], plot_df["pred"]), 2))

r2:  0.84
MAE:  0.13
RMSE:  0.07

	Temperature	FCU-RA_T	FCU-SP	FCU-ST	FCU-WST	PAH-VFD_SPD	PAH-ST1	PAH-SA_T	Floor_CO2_mean	PET_Indoor	SP_Target
Time
2021-04-01 00:00:00+08:00	24.500000	22.313543	23.1	0.0	3.0	0.0	0.0	22.400000	481.424007	22.520543	23.0
2021-04-01 00:10:00+08:00	24.483333	22.300000	23.1	0.0	3.0	0.0	0.0	22.400000	480.063036	22.518000	23.2
2021-04-01 00:20:00+08:00	24.466667	22.300000	23.1	0.0	3.0	0.0	0.0	22.400000	478.947236	22.517000	23.2
2021-04-01 00:30:00+08:00	24.450000	22.280525	23.1	0.0	3.0	0.0	0.0	22.400000	477.952797	22.501525	23.2
2021-04-01 00:40:00+08:00	24.433333	22.200000	23.1	0.0	3.0	0.0	0.0	22.400000	477.826316	22.444000	23.3
...	...	...	...	...	...	...	...	...	...	...	...
2021-08-31 23:10:00+08:00	29.350000	23.697488	25.0	0.0	3.0	0.0	0.0	24.000000	427.503254	23.921000	25.0
2021-08-31 23:20:00+08:00	29.300000	23.600000	25.0	0.0	3.0	0.0	0.0	24.068112	427.301354	23.920000	25.0
2021-08-31 23:30:00+08:00	29.250000	23.600000	25.0	0.0	3.0	0.0	0.0	24.149771	426.716231	23.920000	25.0
2021-08-31 23:40:00+08:00	29.200000	23.567579	25.0	0.0	3.0	0.0	0.0	24.254774	426.336100	23.920000	25.0
2021-08-31 23:50:00+08:00	29.150000	23.500000	25.0	0.0	3.0	0.0	0.0	24.300000	426.310519	23.919000	25.0

	Temperature	Hour	DoW	HoW	FCU-RA_T	FCU-SP	FCU-ST	FCU-WST	PAH-VFD_SPD	PAH-ST1	...	HoW-lag	FCU-RA_T-lag	FCU-SP-lag	FCU-ST-lag	FCU-WST-lag	PAH-VFD_SPD-lag	PAH-ST1-lag	PAH-SA_T-lag	Floor_CO2_mean-lag	PET_Indoor-lag
Time
2021-04-01 01:00:00+08:00	0.383178	0.041958	0.500000	0.434955	0.145985	0.183333	0.0	1.0	0.0	0.0	...	0.428997	0.166704	0.183333	0.0	1.0	0.0	0.0	0.664688	0.708873	0.522040
2021-04-01 01:10:00+08:00	0.383956	0.048951	0.500000	0.435948	0.145985	0.183333	0.0	1.0	0.0	0.0	...	0.429990	0.164233	0.183333	0.0	1.0	0.0	0.0	0.664688	0.706869	0.521720
2021-04-01 01:20:00+08:00	0.384735	0.055944	0.500000	0.436941	0.145985	0.183333	0.0	1.0	0.0	0.0	...	0.430983	0.164233	0.183333	0.0	1.0	0.0	0.0	0.664688	0.705226	0.521594
2021-04-01 01:30:00+08:00	0.385514	0.062937	0.500000	0.437934	0.145985	0.183333	0.0	1.0	0.0	0.0	...	0.431976	0.160679	0.183333	0.0	1.0	0.0	0.0	0.664688	0.703762	0.519643
2021-04-01 01:40:00+08:00	0.386293	0.069930	0.500000	0.438928	0.145985	0.183333	0.0	1.0	0.0	0.0	...	0.432969	0.145985	0.183333	0.0	1.0	0.0	0.0	0.664688	0.703576	0.512391
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-08-31 23:10:00+08:00	0.614486	0.972028	0.166667	0.281033	0.419247	0.500000	0.0	1.0	0.0	0.0	...	0.275074	0.528267	0.500000	0.0	1.0	0.0	0.0	0.685993	0.631985	0.734818
2021-08-31 23:20:00+08:00	0.612150	0.979021	0.166667	0.282026	0.401458	0.500000	0.0	1.0	0.0	0.0	...	0.276068	0.499538	0.500000	0.0	1.0	0.0	0.0	0.693065	0.631117	0.719315
2021-08-31 23:30:00+08:00	0.609813	0.986014	0.166667	0.283019	0.401458	0.500000	0.0	1.0	0.0	0.0	...	0.277061	0.476726	0.500000	0.0	1.0	0.0	0.0	0.697126	0.630624	0.708473
2021-08-31 23:40:00+08:00	0.607477	0.993007	0.166667	0.284012	0.395541	0.500000	0.0	1.0	0.0	0.0	...	0.278054	0.456350	0.500000	0.0	1.0	0.0	0.0	0.702158	0.630276	0.698809
2021-08-31 23:50:00+08:00	0.605140	1.000000	0.166667	0.285005	0.383209	0.500000	0.0	1.0	0.0	0.0	...	0.279047	0.440235	0.500000	0.0	1.0	0.0	0.0	0.706231	0.630140	0.698833

	SP_Target
Time
2021-04-01 01:00:00+08:00	0.366667
2021-04-01 01:10:00+08:00	0.366667
2021-04-01 01:20:00+08:00	0.366667
2021-04-01 01:30:00+08:00	0.366667
2021-04-01 01:40:00+08:00	0.366667
...	...
2021-08-31 23:10:00+08:00	0.666667
2021-08-31 23:20:00+08:00	0.666667
2021-08-31 23:30:00+08:00	0.666667
2021-08-31 23:40:00+08:00	0.666667
2021-08-31 23:50:00+08:00	0.666667